一种基于仿生元胞自动机的图像加密算法

针对一维元胞自动机加密密钥空间小、扩散速度慢、安全性不高,以及有些二维元胞自动机需额外增加规则存储空间,加密后效果不理想等问题,提出一种基于仿生元胞自动机的图像加密算法。该算法首先将明文图像像素值转换为二进制矩阵,再将二进制矩阵分成大小相同的2部分作为仿生元胞自动机的2个初始状态,然后选取一个仿生元胞自动机规则作为密钥,利用仿生元胞自动机的演化进行加密。实验结果表明,本文提出的图像加密方案具有较大的密钥空间,对明文和密钥敏感,可以更好地保障加密图像的安全。     

基于二维CA和混沌系统的图像加密新算法

基于二维元胞自动机和Logistic混沌映射,提出了一种新的图像加密算法.该算法主要思想是采用Logistic映射设计一种非线性耦合结构来对明文像素矩阵进行置乱,然后在分析元胞自动机的混沌和密码学性质的基础上构造一个二维伪随机数矩阵来进行图像加密.仿真实验结果表明,该算法具有较大的密钥空间,对密钥具有极高的敏感性,密文具有良好的扩散和统计特性,可以有效地抵御穷举攻击、敏感性攻击以及统计攻击等.     

耦合触发元胞自动机在数据加密中的应用

利用元胞自动机之间的相互作用，提出耦合触发元胞自动机的加密技术，增加密钥空间和增强密码系统的强度．分析结果表明该算法可以抵抗蛮力攻击和差分分析攻击，具有较高的安全性和很强的实用性．     

耦合双触发元胞自动机的加密技术研究

针对单向触发元胞自动机加密中误差传播的相似性问题,以及双向触发元胞自动机中密钥空间小的问题,提出了耦合双触发元胞自动机的加密技术,通过相互作用、相互影响的元胞自动机系统之间的共同演化,反向迭代完成数据加密,正向演化完成数据解密,达到解决密文相似性问题以及增加密钥的目的.分析结果表明,该算法可以抵抗蛮力攻击和已知明文、已知密文以及差分分析攻击,具有较高的安全性和很强的实用性.     

区域耦合状态转移概率化元胞自动机模型

针对多区域系统内部各个子区域属性的差异化,提出多区域耦合状态转移概率化元胞自动机模型。在该模型中,运用区域耦合原理和耦合区域信息交换规则来构造元胞自动机（CA）模型的演化规则,实现局部子区域之间的信息交换,运用不同类型的元胞自动机模型对子区域分别建立不同的CA模拟模型,并用概率化方法处理模拟模型中元胞状态的转化,实现各个子区域状态的演化。实验表明,子区域在保持自身演化形态相对独立的同时,通过边界区域耦合进行演化连接,各个子区域内的元胞能与其边界耦合区域内的元胞保持一种相对稳定的有序形态,且一个子区域的影响能在一定程度上传递到其他子区域内。该模型能很好地处理大规模复杂区域环境下的空间演化模拟。     

基于耦合触发元胞自动机的并行加密模型

介绍了耦合触发元胞自动机构造的一般方法，根据耦合触发元胞自动机的特性，提出了一种耦合触发元胞自动机并行加密模型．仿真实验表明：耦合触发元胞自动机并行加密模型与其迭代加密模型相比，大大提高了加密解密速度，具有更好的实时性、普适性．     

一种基于元胞自动机的无向图剖分优化算法

运用元胞自动机理论,针对无向图剖分优化问题进行了分析和建模,提出了一种元胞自动机模型以及基于该模型的无向图剖分优化算法。在该元胞自动机模型中,元胞对应于无向图中的结点,元胞的邻居对应于邻接结点,元胞空间对应于无向图中的结点集,元胞的状态对应于所在的结点子集。实验及分析表明该算法不仅能找到无向图的近似最优剖分,而且有效地降低了空间复杂度和时间复杂度。     

新型一维偏移耦合映像格系统及其动态特性

混沌系统具有复杂的动力学行为,但在数字系统中运行时会出现动力学特性退化的问题。元胞自动机在时间、空间上都具有离散性,能够有效减弱混沌系统在有限精度下的动力学退化问题。基于元胞自动机,提出了一种一维偏移耦合映像格系统,利用初等元胞自动机每次更新的不同状态,动态产生每个格子的耦合索引偏移量,再根据偏移量对混沌序列施加不同的扰动,然后交替切换元胞自动机的迭代规则。最后,对混沌系统的动力学特性进行对比分析以及对该系统产生的时间序列进行量化和随机性检测,仿真实验结果表明,该混沌系统周期更长,遍历性更好,产生的序列随机性更佳,在序列密码算法中有很大的应用价值。     

基于元胞自动机模型的新型二值图像压缩算法

不同于传统的去相关,去冗余的压缩方法,提出一种基于元胞自动机模型的二值图像压缩算法。该算法用遗传规划算法搜索出较优的元胞自动机规则后,对分块后的二值图像矢量进行元胞自动机变换,利用元胞自动机的变换状态多样性等特点,生成相邻矢量,将变换次数作为码本。实验表明：该算法经过4次以内的元胞自动机变换即可生成较优的相邻矢量,具有编码时间短、重建图像的质量好、压缩率高、适应性强等特点,并且与其它压缩算法结合性好。     

融合Hénon映射和元胞自动机的图像加密算法

为增强数字图像在通信传输中的安全性,提出了一种结合了 Hénon映射和元胞自动机优良特性的图像加密算法.该算法先将明文图像转换为二进制矩阵,根据外部密钥使用Hénon映射采取逐级迭代法产生伪随机序列,依据伪随机序列采用初等元胞自动机演化规则对图像信息逐行逐列地进行扩散,迭代3次后得到加密图像.经过仿真实验表明,该图像加...     

基于元胞自动机扩展模型的图的最短路径算法

利用元胞自动机在元胞空间上的并行特性，采用元胞动态邻居，时间段自适应调整的方法，构造出一种新的基于元胞自动机扩展模型的最短路径搜索算法，即通过简单规则的元胞状态演化，得到带权图的最短路径；该方法经过优化，能够达到Dijkstra算法的时间效率；并且为基于元胞自动机扩展模型解决图的问题的提供了新的思路。     

细胞自动机及其在复杂系统研究中的应用

近年来,随着计算机技术的飞速发展,细胞自动机理论及其在复杂系统研究领域中应用的研究受到科学界的高度重视。由于细胞自动机具有结构简单、并行计算能力强和易于在计算机上实现系统的可视化模拟等特点,它已成为复杂系统研究领域一种具有广阔发展前景的新方法,在复杂系统(如:生物系统、交通系统、经济系统、环境系统、工程系统、社会系统和各种工业生产系统等)研究中得到了广泛的应用,并取得了令人瞩目的成果。文章在简要介绍复杂系统、细胞自动机概念及特点的基础上,分析了二者的相互关系,较详细地综述了细胞自动机在各种复杂系统研究领域的应用,最后结合笔者的研究指出了细胞自动机理论及其在复杂系统应用研究中有待进一步完善和深入研究的问题。     

地下矿CA粒子系统火灾蔓延仿真

为了对火灾在地下矿巷道中的蔓延情况进行仿真,提出了一个元胞自动机理论和粒子系统理论相结合的地下矿巷道系统中火灾蔓延仿真方法。针对地下矿巷道这个特殊环境,采取可变邻域半径的元胞自动机模型来仿真火焰在巷道中蔓延的情景;同时,引入基于粒子系统的烟雾模型,把燃烧着的元胞作为烟雾模型的粒子发射器。整个仿真系统基于同一时钟运行,把时间、空间、火烟的物理行为有效地结合起来,不仅能非常直观地仿真出火势蔓延态势和烟雾扩散运动过程,并且通过在仿真过程中计算记录不同时刻火灾蔓延距离和入侵巷道烟雾粒子数量等数据,可以获取大量巷道环境相关信息,为逃生决策和火灾预防与控制提供依据。     

一维单车道交通流元胞自动机模型综述

随着交通流模拟的需要及智能交通系统的发展,出现了基于元胞自动机理论的交通流模型。交通流模型是交通理论研究的重要问题,交通流元胞自动机模型的出现和发展为交通流理论的研究提供了一种新的方向。文章主要介绍了单车道交通流元胞自动机模型,并对元胞自动机模型的发展提出了展望。     

基于二维X型可逆细胞自动机的加密算法

针对二维传统邻居类型细胞自动机结构和演化复杂、加密效率低,而一维细胞自动机密钥空间小、扩散速度慢、需要多轮迭代才能产生雪崩效应的问题,提出一种新的基于二维X型可逆细胞自动机并引入Arnold变换的加密算法。首先利用提出的细胞自动机对明文序列进行演化;在每一步演化结束后再对序列进行Arnold变换和循环移位变换;最后直到加密的密文满足要求,停止演化和变换。通过实验证明,密钥空间增大16.8%,可以抵抗穷举攻击;另外扩散性和混淆性好,能产生雪崩效应,可抵抗选择明文攻击。     

Sand automata as cellular automata

In this paper, we exhibit a strong relation between the sand automata configuration space and the cellular automata configuration space. This relation induces a compact topology for sand automata, and a new context in which sand automata are homeomorphic to cellular automata acting on a specific subshift. We show that the existing topological results for sand automata, including the Hedlund-like representation theorem, still hold. In this context, we give a characterization of cellular automata which are sand automata, and study some dynamical behaviors such as equicontinuity. Furthermore, we deal with simple sand automata. We show that the classical definition of nilpotency is not meaningful for sand automata. Then, we introduce the suitable new notion of flattening sand automata. Finally, we prove that this simple dynamical behavior is undecidable.     

Asynchronous cellular learning automata

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous cellular learning automata) is needed. In this paper, we introduce asynchronous cellular learning automata and study its steady state behavior. Then an application of this new model to cellular networks has been presented.     

An unsupervised classifier for remote-sensing imagery based on improved cellular automata

Traditional unsupervised classification algorithms for remote-sensing images, such as k-means (KM), have been widely used for massive data sets due to their simplicity and high efficiency. However, they do not usually take the interaction between neighbouring pixels into account, but only take individual pixels as the elements for clustering and classification. According to Tobler’s first law of geography, everything is related to everything else, but near things are more related than distant things. To make use of the spatial interaction between pixels, the cellular automata method can be employed to improve the accuracy of image classification. In cellular automata theory, the state of a cell at the next moment is determined by its current state and that of its neighbours. In traditional cellular automata methods, which are based on a standard neighbour configuration, even if the influence of neighbouring cells on the central cell is measured, the weights of these influences are the same. Hence, this article proposes an improved cellular automata method for image classification by allowing the cellular automata to diffuse in a geometrical circle, and by measuring the influence of the neighbouring cells using a fuzzy membership function. The proposed classifier was tested with typical Landsat Enhanced Thematic Mapper Plus (ETM+) and high-resolution images. The experiments reveal that the new classifier can achieve better results, in terms of overall accuracy and kappa coefficient, than cellular automata classifier based on Moore type (CAS), KM, and fuzzy c-means.     

Structure and reversibility of 2D hexagonal cellular automata

Cellular automata are used to model dynamical phenomena by focusing on their local behavior which depends on the neighboring cells in order to express their global behavior. The geometrical structure of the models suggests the algebraic structure of cellular automata. After modeling the dynamical phenomena, it is sometimes an important problem to be able to move backwards in order to understand it better. This is only possible if cellular automata is reversible. In this paper, 2D finite cellular automata defined by local rules based on hexagonal cell structure are studied. Rule matrix of the hexagonal finite cellular automaton is obtained. The rank of rule matrices representing the 2D hexagonal finite cellular automata via an algorithm is computed. It is a well known fact that determining the reversibility of a 2D cellular automata is a very difficult problem in general. Here, the reversibility problem of this family of 2D hexagonal cellular automata is also resolved completely.     

Simulation of dynamic phenomena by cellular automata

This paper is aimed at showing how cellular automata can be conveniently employed to simulate dynamic phenomena, typically involving transportation, diffusion, or propagation problems. A cellular automaton can be viewed as made of two parts: a computational engine based on a proper discretization of the domain and charged with correctness and consistency controls, and a dynamic model constituted by transition functions that express cell behaviour. The adoption of cellular automata introduces a new means of spatial data modelling, in addition to those traditionally provided by GIS packages, resulting in the possibility of storing elements of dynamic knowledge in cellular maps: each cell is provided with the attributes that constitute its state, and groups of cells with the functions that describe their mutual interaction. The basic characteristics of cellular automata are discussed with reference to a significant application case, the study of tide propagation over a lagoon.